The present invention is directed to methods and apparatus which monitor power line waveform that is affected by an electrical device, such as for example an induction machine, during actual in-service operation of the machine in its working environment. Based upon analysis of the line waveform signature, the present invention predicts potential machine failure caused by defects in machine mechanical components, such as broken rotor bars and defective rolling element bearings which support the machine shaft.
Electric induction motors are a workhorse of any industrial or commercial facility. They are used for many applications ranging from driving compressors and pumps, to blowers and machine tools. It is not uncommon to have thousands of such motors in one industrial facility.
Although motors are simple and reliable machines, their annual failure rate is conservatively estimated at 5% per annum. In some industries, such as pulp and paper processing, this failure rate can be as high as 12% annually. According to market research of the present owner of this application, 3.4 million so-called National Electrical Manufacturers Association (NEMA)-rated motors, which are defined as three phase, 600 Volt AC or less, and 400 HP or less, are sold annually in the United States of America. Over 80% of these motors are 20 HP or less, The installed base in the United States alone is assumed to be approximately 50 million motors.
Down time in a factory can be extremely expensive, and in many cases far exceeds the replacement and installation cost of the motor itself. According the Electric Power Research Institute (EPRI) and the Institute of Electrical and Electronic Engineers (IEEE), the majority of motor failures, 51% to 64%, are the result of, or result in mechanical failures, and 35% to 37% are due to thermal failures of the motor insulation.
The vast majority electric induction motors now installed in the United States of America and the other developed industrial nations are protected by thermal overload relays, based on 40-year-old technology. These relays attempt to emulate the thermal behavior of the motor; however, they fall far short because their time constant utilized for heat transfer modelling is far less than the true thermal time constant of the motor, resulting in inadequate thermal protection. A new generation of microprocessor-based relays which provide far better modeling of the motor thermal behavior have been introduced recently; however, their installed base is very small, less than 1%. Neither the thermal nor the microprocessor-based motor protection relays provide protection against motor mechanical component failures. This explains the relatively high percentage of motor failures.
Many failures in electric machines are the result of, or result in abnormally high vibrations in the machine itself. Undesirable vibrations can be caused by mechanical problems in the machine, such as worn or defective bearings, eccentricities in the rotor or stator, or any number of other structural deteriorations. In addition, machines which are subject to high vibrations from any source are prone to failure, especially due to shorted stator windings and insulation breakdown.
There has been a long-felt need in the motor industry to identify undesirable motor mechanical vibrations and to attempt to correlate identified vibrations with specific operational fault conditions which cause the specific vibration. In the past, many industrial plants would rely on skilled mechanics and technicians who would examine each running motor individually by hand. The mechanic would rely on his or her skilled tactile, auditory and visual senses, along with general intuitive experience, to correlate operational sounds and vibration with specific problems. For example, a faulty rolling element bearing race would have a different vibrational amplitude or frequency than a rotor bar developing a mechanical fault. Such direct hand sensing of potential faults by a skilled mechanic is dependent upon such factors as the mechanic's skill level, the frequency of inspections, the difficulty of carrying out inspections of many motors in hazardous or hard-to-reach environments, all combined with a healthy dose of luck that the mechanic inspects sufficiently before failure in order to correct the problem before it causes factory downtime. A modern industrial plant cannot afford to rely on such widely disseparate factors, hence in the past, many mechanized techniques have been tried to predict motor failure.
One long-known, but difficult and expensive solution has been to adopt aggressive preventive maintenance schedules, with the hope that components on average are replaced before they are likely to fail. Using industrial engineering statistical models of mean time between failure, parts such as bearings are routinely replaced well before statistical predicted failure. Unfortunately, statistics do not predict the failure of a specific individual component. A specific bearing rated for replacement every 5,000 running hours may in fact fail within a few hundred hours.
Another known failure prediction technique has been to measure actual mechanical vibration with vibration sensors mounted on the machine. The spectral component content (i.e., the magnitude and frequency of vibration) of the vibration excitation signal can then be used to determine if a problem exists in the machine, or if one of the above-mentioned failure modes is likely. Then, the underlying task is to determine how the vibrations in electric machines relate to physical defects in the machine and, more fundamentally, what types of vibrations (i.e., vibration component frequencies and magnitudes) indicate possible machine failures. Such systems have their own problems. For one, addition of a vibration sensor and related electronics adds expensive components to the system. These sensing components are relatively too delicate for industrial environments and are prone to failure. When the vibration sensing systems are operated continuously in a relatively high vibration noise environment of a factory, they can trigger false failure readings. Isolation of line and load noise by diagnostic bench testing motors in a repair shop is not a viable option in most industrial environments, because of difficulties in motor removal and reinstallation. The factory cannot be shut down frequently to remove and replace motors for diagnostic testing, except perhaps during defined plant down time, such as on holidays.
Many problems in electric machines, including all of those which cause undesirable vibrations, result in unwanted harmonic electric currents in the machine. These are due to nonuniformaties in the air gap flux, or oscillations (vibrations) in the mechanical load torque on the machine. In either case, the defective machine is no longer modeled by a constant parameter circuit in steady-state operation. As a result, harmonic currents exist in the stator. The harmonic content of the stator current can then be used to determine not only undesirable vibrations in the machine, but also electrical problems, such as damaged or broken rotor bars and end rings, and damaged stator windings. Unfortunately, as with mechanical vibration noise, industrial environments are also rich in electrical distribution system harmonic noise. Thus, known electrical current signature analyzers cannot associate power waveform characteristics with motor operating characteristics; they have been of the nature of bench analyzers rather than in-service, active diagnostic tools which are continuously observing operational motors on the factory floor.
Examining more closely the known electric induction machine direct mechanical vibration measurement and current harmonic analysis testing systems, each has strengths and weaknesses. Direct vibration measurement has the obvious advantage of measuring the harmful vibrations directly through the use of one or more piezoelectric vibration sensors. In addition, signal processing hardware is reduced since the signature to be analyzed need not be extracted from a much higher magnitude carrier signal, as in the case with current harmonic analysis. The disadvantage of the direct measurement is the fact that the machine must be outfitted with the sensor, and the sensor with its wiring is prone to failure. The current harmonic analysis has the advantage of not requiring any sensors in addition to those which already exist in currently available motor controllers. However, current harmonic analysis is computationally difficult since the harmonics in question are many times smaller in magnitude than the excitation frequency (i.e., 50 or 60 Hertz electric utility power). In addition, an understanding must be gained of the relationship between the current harmonics and machine vibrations and resultant motor electrical performance deterioration.
Commercial products are available which detect induction machine incipient fault by current signature harmonic analysis. Generally, the products include a personal computer which performs motor current signature analysis and displays the results of this analysis. The products are basically spectrum analyzers which indicate possible machine problems associated with each distinct harmonic identified in the current spectrum. All of these products are intended to be used as maintenance tools and are not specifically intended for continuous on-line use. This is essentially due to the fact that a trained user must interpret the information provided by the spectral analysis and associate the information with a machine operating condition. Present harmonic analysis systems are also relatively expensive.
Technical literature on induction machine fault detection using current spectral analysis deals with the subject of detecting faults in the rotor of the machine. The problem of broken rotor bars, most common in large machines, has been studied.
Previously known induction machine current signature harmonic analysis systems have the following disadvantages:
1. Known systems require a human expert to interpret the motor signature and associate the motor signature with a motor operating condition, in order to determine if the motor is "good" or "bad".
2. Known systems are relatively expensive, and therefore are limited for use with large, expensive motors. Typical cost for these systems is between U.S. $20,000 and $50,000.
3. Known systems are designed specifically to detect broken rotor bars, a common failure mode in very large motors but infrequent in small motors.
4. Known systems require detailed structural knowledge of the motor design, such as the number of stator slots, rotor bars and quantity of rolling elements in shaft bearing.
5. Known systems are intended for use as a maintenance tool to perform bench tests on individual motors that are not driving loads.
There has been a long-felt need in the motor industry for methods and apparatus for predicting electric induction machine failures in an "on-line", "in-service" fashion, without additional sensors; that is, while the machine is in use, so that the user/operator can be notified when a machine failure is imminent without removing the machine from service. The needed methods and apparatus must also take into account the fact that the specific physical characteristics of the machine are not known. Since it is desired that failures be predicted in any arbitrary machine connected to the present invention apparatus, the prediction scheme must work without the knowledge of the machine design parameters. The on-line failure prediction scheme can then be incorporated into a microcomputer-based motor controller, which gives the user a significantly higher degree of motor protection than is currently available.